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Scholarship on the political gender gap in the United States has attributed women's political views to their greater compassion, yet individual‐level measures of compassion have almost never been used to directly examine such claims. We address this issue using the only nationally representative survey to include psychometrically validated measures of compassion alongside appropriate political variables. We show that even though women are more compassionate in the aggregate than men in some respects, this added compassion does not explain the gender gap in partisanship. Female respondents report having more tender feelings towards the less fortunate, but these empathetic feelings are not associated with partisan identity. Women also show a slightly greater commitment to a principle of care, but this principle accounts for little of the partisan gap between men and women and has no significant relationship with partisanship after accounting for gender differences in egalitarian political values.

AbstractThis paper analyzes the relationship between unexplained racial/ethnic wage differentials on the one hand and social network segregation, as measured by inbreeding homophily, on the other. Our analysis is based on both the US and Estonian surveys, supplemented with the Estonian telephone communication data. In the case of Estonia we consider the regional variation in economic performance of the Russian minority, and in the US case we consider the regional variation in black--white differentials. Our analysis finds a strong relationship between the size of the wage differential and network segregation: Regions with more segregated social networks exhibit larger unexplained wage gaps.

The last decade has seen substantial advances in statistical techniques for the analysis of network data, as well as a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis—the quadratic assignment procedure, exponential random graph models, and latent space network models—highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This article introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and it helps researchers choose which model to use in their own research.